DocumentCode :
2385761
Title :
Equipping robot control programs with first-order probabilistic reasoning capabilities
Author :
Jain, Dominik ; Mösenlechner, Lorenz ; Beetz, Michael
Author_Institution :
Intelligent Autonomous Systems, Technische Universitÿt Mÿnchen, Germany
fYear :
2009
fDate :
12-17 May 2009
Firstpage :
3626
Lastpage :
3631
Abstract :
An autonomous robot system that is to act in a real-world environment is faced with the problem of having to deal with a high degree of both complexity as well as uncertainty. Therefore, robots should be equipped with a knowledge representation system that is able to soundly handle both aspects. In this paper, we thus introduce an architecture that provides a coupling between plan-based robot controllers and a probabilistic knowledge representation system based on recent developments in statistical relational learning, which possesses the required level of expressiveness and generality. We outline possible applications of the corresponding models in the context of robot control, discussing suitable representation formalisms, inference and learning methods as well as transparent extensions of a robot planning language that allow robot control programs to soundly integrate the results of probabilistic inference into their plan generation process.
Keywords :
Concrete; Context modeling; Control systems; Humans; Intelligent robots; Intelligent systems; Knowledge representation; Robot control; Robotics and automation; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation, 2009. ICRA '09. IEEE International Conference on
Conference_Location :
Kobe
ISSN :
1050-4729
Print_ISBN :
978-1-4244-2788-8
Electronic_ISBN :
1050-4729
Type :
conf
DOI :
10.1109/ROBOT.2009.5152676
Filename :
5152676
Link To Document :
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